Diffusion magnetic resonance imaging (diffusion MRI) combines nuclear magnetic resonance (NMR) imaging principles with those that encode molecular diffusion in the NMR signal by magnetic field gradient pulses. Molecular diffusion refers to the random translational motion of molecules, also called Brownian motion that results from the thermal energy carried by these molecules. The rationale for diffusion MRI is that during the random and diffusion driven displacements, these molecules probe tissue structure at a microscopic scale well beyond the usual image resolution available in other imaging technologies.
Water is the most convenient molecular species to study using diffusion MRI, although other metabolites may also be studied. During typical diffusion times of about 50 msec, water molecules may move in tissue, such as brain tissue, over distances of about 10 μm while bouncing, crossing, or interacting with many other tissue components, such as cell membranes, fibers, or macromolecules. For example, the overall effect observed in a diffusion MRI image voxel of several mm3 reflects on a statistical basis the displacement distribution of the water molecules present within this particular diffusion MRI image voxel. The observation and analysis of this displacement distribution may provide unique insights into the structure and geometric organization of tissues.
Diffusion, as governed by Fick's law, may be expressed as:J=−D∇(C)  (1)
wherein C is the concentration, D is the diffusivity, and J is the flux vector.
Diffusion MRI characterizes the diffusion coefficient, D, by experimental measurements. In particular, diffusion MRI makes use of the attenuation of the diffusion MRI signal due to the diffusion and resulting random movement of water molecules over time. For a single diffusion MRI image, this attenuation, A, may be expressed as:A=e−(bD)  (2)
wherein b, or the “b-value”, is a numerical quantity used to characterize the timing, amplitude, and shape of magnetic gradients pulses used in the diffusion MRI acquisition sequence.
In biological tissues water diffusion may be an anisotropic process, where the b-value and the diffusion coefficient D may be characterized as tensors and the diffusivity of water molecules may be captured using tensor characterizations. However, for many purposes, apparent diffusion coefficient (ADC), which is a single scalar quantity, may be used to capture the value of water diffusivity within a given sample along a particular direction.
The ADC may be calculated by acquiring two or more diffusion MRI images using different magnetic field gradient durations or amplitudes, thereby resulting in different b-values for each diffusion MRI image derived using equation (2). The contrast in an ADC map derived from a comparison of two or more diffusion MRI images depends on the spatially distributed diffusion coefficients of the acquired tissues and excludes effects from T1 or T2* relaxations. By acquiring multiple diffusion MRI images with different b-values, and then plotting the logarithm of signal intensity as a function of these b-values, the ADC may be determined from the slope of the acquired plotted data.
Diffusion MRI has been applied clinically to detect brain ischemia, following the discovery in a cat brain model that water diffusion drops at a very early stage of an ischemic event. The increased sensitivity of diffusion-weighted diffusion MRI in detecting acute ischemia is due to the restricted intracellular motion of water protons in a manner similar to cytotoxic edema. A decreased ADC, as measured by a diffusion MRI device, is a sensitive indicator of early brain ischemia that has gained widespread acceptance. By detecting changes in water diffusivity, diffusion MRI may provide patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable.
In order to develop and implement clinical diagnostic criteria that incorporate diffusion MRI measurements, the diffusion MRI data must be obtained in a precise, accurate and repeatable manner. However, the diffusion MRI data may vary between different diffusion MRI devices, or the data may vary for the same diffusion MRI device over time due to gradual changes in the hardware of the diffusion MRI device or updates in data acquisition software used by the diffusion MRI device to process the acquired data. In order to achieve a suitable level of precision and accuracy, periodic calibration of the diffusion MRI device is essential.
It is known that diffusion MRI devices may employ a phantom calibration body to calibrate these types of devices. Existing phantom calibration bodies have been constructed from gels and polymer materials to calibrate various aspects of the diffusion MRI device; however, none of these existing phantom bodies are stable, non-flammable, non-toxic, transportable, vibration dampening, and/or exhibit isotropic diffusion properties within a range of viscosities. In addition, common additives, including contrast agents such as magnesium chloride and gadolinium-diethylenetriamine penta-acetic acid (Gd-DTPA) have been incorporated into the materials of existing phantom calibration bodies. However, these additives failed to alter the diffusion properties sufficiently to mimic the diffusivity of tissues suitable for calibrating a diffusion MRI device.
As such, there exists a need in the art for a phantom calibration body with known and controllable water diffusivity characteristics that mimic those in the tissues to be subjected to diffusion MRI. Further, there exists a need for a method of making a phantom calibration body in which the water diffusivity may be adjusted to match the diffusivity of the particular tissues to be subjected to diffusion MRI. There also exists a need in the art for a method of calibrating a diffusion MRI device for diffusion and image resolution characteristics using the phantom calibration body.